AntTrust: An Ant-Inspired Trust Management System for Peer-to-Peer Networks

In P2P networks, self-organizing anonymous peers share different resources without a central entity controlling their interactions. Peers can join and leave the network at any time, which opens the door to malicious attacks that can damage the network. Therefore, trust management systems that can ensure trustworthy interactions between peers are gaining prominence. This paper proposes AntTrust, a trust management system inspired by the ant colony. Unlike other ant-inspired algorithms, which usually adopt a problem-independent approach, AntTrust follows a problem-dependent (problem-specific) heuristic to find a trustworthy peer in a reasonable time. It locates a trustworthy file provider based on four consecutive trust factors: current trust, recommendation, feedback, and collective trust. Three rival trust management paradigms, namely, EigenTrust, Trust Network Analysis with Subjective Logic (TNA-SL), and Trust Ant Colony System (TACS), were tested to benchmark the performance of AntTrust. The experimental results demonstrate that AntTrust is capable of providing a higher and more stable success rate at a low running time regardless of the percentage of malicious peers in the network.

EXPRESS: Job Demands and Burnout: The Multilevel Boundary Conditions of Collective Trust and Competitive Pressure

Do high job demands help employees staying challenged at work, or do they challenge their well-being? Despite burnout being an ever-pressing matter in contemporary workspaces, the understanding of the link between job demands and burnout remains limited, especially considering the important multilayered role of context in organizations. Our study develops an integrated perspective on the antecedents of burnout, rather than viewing various elements in isolation. Specifically, we uncover a three-way interaction among job demands, collective trust, and competitive pressure across the three levels of study via a multilevel analysis of 5,485 employees, nested into 2,872 units in 89 German organizations. The three-way interaction of individual-level job demands with unit-level collective trust depends on the magnitude of competitive pressure at the organizational level. In a condition of low organizational-level competitive pressure, unit-level trust can mitigate the positive effect of individual job demands on burnout. Our findings indicate that job demands can be a double-edged sword, bringing with them both benefits and burdens. From a practical perspective, we provide guidance for organizations on how to maintain high job demands by emphasizing collective trust and open communication about organizational-level competitive pressure to mitigate burnout at work.

Acquiring knowledge: Epistemic trust in the age of fake news

The alarming spread of fake news and the breakdown of collective trust in sources of information is a major ongoing concern. Public mistrust and conspiracy beliefs canchange behaviour in a way that profoundly alters society’s reaction to new information. However, we still lack a broad psychological and socio-evolutionary understanding ofthe transmission of knowledge: the concept of epistemic trust (defined as trust in communicated knowledge) could provide the basis for such an integrated understanding. This study examined the role of epistemic trust in determining individualcapacity to recognise fake and real news, and susceptibility to conspiracy thinking – both in general and in relation to COVID-19. Measuring three different epistemic dispositions – trusting, mistrusting and credulous – in two different studies (study 1 = 705; study 2 = 502), we found that Credulity is associated with inability to discriminate between fake and real news. To explore the developmental factors at work in creating vulnerability to fake news, we investigated the mediating role of Mistrust and Credulity, and found that these factors mediated the relationship between exposure to childhood adversity and the failure to distinguish between fake and real news. Both Mistrust and Credulity were also associated with general and COVID-19 related conspiracy beliefs; similarly, Mistrust and Credulity were associated with vaccine hesitancy, both in general and in relation to COVID-19. Findings illuminate the potential psychological processes at work in generating broad social-political phenomena such as fake news and conspiracy thinking.

Can Trust Motivate Farmers to Purchase Natural Disaster Insurance? Evidence from Earthquake-Stricken Areas of Sichuan, China

Natural disasters cause great losses of property and life in many areas of China. However, rural residents do not always insure themselves against these losses. Measuring the correlation between trust and farmers’ behavior related to the purchasing of natural disaster insurance is of great significance to the implementation of natural disaster insurance pilot programs and insurance systems in China. This article analyzes data from a survey of 327 households in four districts and counties of Sichuan Province, China, that were affected by the Wenchuan and Lushan earthquakes. According to the relevant theories of trust, trust was divided into three dimensions: authority trust, collective trust, and relationship trust. Then a technology acceptance model was built, and PLS-SEM was used to comprehensively analyze the correlation between different dimensions of trust and farmers’ insurance purchase behavior. The results show that (1) only relationship trust was directly and significantly positively correlated with insurance purchasing behavior. Although there was no direct significant correlation between authoritative trust or collective trust and buying behavior, relationship trust was found to indirectly affect buying behavior. (2) Younger farmers and those with higher incomes are more likely to buy disaster insurance if they live in a disaster-threat zone, have experienced disasters, and are risk averse. We then discuss the correlations between farmers’ trust and natural disaster insurance purchasing in areas threatened by earthquake disasters. This provides a policy inspiration for the promotion of disaster insurance and the construction of insurance systems in China.

Blockchain-Based Reputation Systems: Implementation Challenges and Mitigation

Reputation expresses the beliefs or opinions about someone or something that are held by an individual or by a community. Reputation Management Systems (RMSs) handle representation, computation, and storage of reputation in some quantitative form, suitable for grounding trust relations among parties. Quantifying reputation is important in situations, like online service provision, which involve interaction between parties who do not know (and potentially distrust) each other. The basic idea is to let parties rate each other. When a party is considered for interaction, its ratings can be aggregated in order to derive a score for deciding whether to trust it or not. While much valuable research work has been done on reputation-based trust schemes, the problem of establishing collective trust in the reputation management system itself has never been fully solved. Recently, several researchers have put forward the idea of using Distributed Ledger Technology (DLT) as the foundation for implementing trustworthy RMSs. The purpose of this paper is to identify some critical problems that arise when DLTs are used in order to manage evidence about previous interaction and compute reputations. The paper proposes some practical solutions and describes methods to deploy them on top of standard DLT of the Ethereum family.

Website Quality, Perceived Flow, Trust, and Commitment

Website quality in online business is still exploratory, and despite growth in building a relationship with customer research, various challenges remain in developing a more customer-oriented website. This chapter tackles the dilemma of how to support website inclusivity in the building of a customer relationship, by investigating flow, commitment-trust, and stimulus-organism-response (SOR) theories. The authors applied the covariance-based SEM (structural equation modeling) to examine the structural model. Primary data for the study comes from 500 respondents through an online questionnaire. The study results reveal that website quality certainly influences users' perceived flow, which in turn positively influences customer trust and CRM. Again, collective trust influences customer commitment and CRM. Finally, collective customer commitment positively controls CRM. Based on the study findings, the theoretical implications, practical inferences, and directions for future study are highlighted.

Exploring metaphor’s effects in reasoning on vaccination

The paper investigates the impact of the use of metaphors in reasoning tasks concerning vaccination, especially for defeasible reasoning cases. We assumed that both metaphor and defeasible reasoning can be relevant to let people understand vaccination as an important collective health phenomenon, by anticipating possible defeating conditions. We hypothesized that extended metaphor could improve both the argumentative and the communicative effects of the message. We designed an empirical study to test our main hypotheses: participants were presented with a text about vaccination, described in either literal or metaphorical terms, based on uncertain vs. safe reasoning scenarios. The results of the study confirmed that defeasible reasoning is relevant for the communicative impact of a text and that an extended metaphor enhances the overall communicative effects of the message, in terms of understandability, persuasion, perceived safety and feeling of control over the health situation, collective trust in expertise and uptake of experts’ advice. However, the results show that this effect is significantly nuanced by the type of defeasible reasoning, especially in the case of participant’s trust in expertise and commitment to experts’ advice.

Automatically Neutralizing Subjective Bias in Text

Texts like news, encyclopedias, and some social media strive for objectivity. Yet bias in the form of inappropriate subjectivity — introducing attitudes via framing, presupposing truth, and casting doubt — remains ubiquitous. This kind of bias erodes our collective trust and fuels social conflict. To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view (“neutralizing” biased text). We also offer the first parallel corpus of biased language. The corpus contains 180,000 sentence pairs and originates from Wikipedia edits that removed various framings, presuppositions, and attitudes from biased sentences. Last, we propose two strong encoder-decoder baselines for the task. A straightforward yet opaque concurrent system uses a BERT encoder to identify subjective words as part of the generation process. An interpretable and controllable modular algorithm separates these steps, using (1) a BERT-based classifier to identify problematic words and (2) a novel join embedding through which the classifier can edit the hidden states of the encoder. Large-scale human evaluation across four domains (encyclopedias, news headlines, books, and political speeches) suggests that these algorithms are a first step towards the automatic identification and reduction of bias.